Regolith, widely distributed on the Earth’s surface, constitutes a significant compartment of the Critical Zone, resulting from intricate interactions among the atmosphere, lithosphere, hydrosphere, and biosphere. Regolith formation critically influences nutrient release, soil production, and long-term climate regulation. Regolith development is governed by two primary processes: production and denudation. An urgent need exists to comprehensively understand these processes to refine our understanding of Critical Zone functions. This study investigates an in-situ regolith profile developed on granitic bedrock from a tropical region (Sanya, China). We conducted geochemical analyses, encompassing major, trace elements and mineralogical compositions as well as U-series isotopes, and applied the U-series disequilibrium method to investigate the formation history of this profile. Alternatively, dividing the regolith profile into sub-weathering zones provides a better explanation for the geochemical results, and a multi-stage model based on this subdivision effectively interprets the evolution of deep regolith. Utilizing this multi-stage model, regolith production rates is derived from the “gain and loss” model, ranging from 1.27 ± 0.03 to 42.42 ± 24.24 m/Ma. The production rates first increase from surface until a maximum rate is reached at the depth of ∼ 160 cm and then decrease at further deeper horizons along the depth profile, and the variation of production rates follows a so-called “humped function”. This pioneering investigation into regolith production rates in the Chinese tropical region indicates that (1) the studied profile deviates from a steady state compared to the denudation rate derived from cosmogenic nuclides (10Be_in-situ); (2) subdividing the deep profile based on geochemical data and U-series isotopic activity ratios is imperative for accurately determining regolith production rates; and (3) the combination of U-series disequilibrium and cosmogenic nuclides robustly evaluates the quantitative evolution state of regolith over long time scales.
The Tasmanian microcontinent, situated along the East Gondwana accretionary margin during the late Neoproterozoic and early Palaeozoic, contains an unequivocal high-pressure metamorphic record comprising key information pertaining to the geodynamics of subduction along the margin. Subduction of the Tasmanian microcontinent is interpreted by some as a response to back-arc basin inversion prior to ophiolite obduction and high-pressure metamorphism during the Cambrian Tyennan Orogeny. However, thermobarometric evidence in support of such a model from rocks once positioned on the subducting continental margin is lacking. Despite occurrences of eclogite-facies mineral assemblages in the strongly deformed Tyennan Region of western Tasmania, garnet-bearing quartzofeldspathic assemblages documented in metasedimentary lithologies from the remote south-west coast of Tasmania have been interpreted as an expression of low- to moderate-pressure metamorphism. We report a strongly overprinted chlorite-quartz-garnet-bearing assemblage from the southern Tyennan Region (Nye Bay) which shows evidence for high-pressure metamorphism. Coarse-grained garnet porphyroblasts contain inclusions of kyanite, muscovite, and rutile, and yield in-situ Lu–Hf dates of c. 520 Ma. The cm-scale garnet porphyroblasts are zoned in the major and trace elements, preserving core-rim compositional gradients reflecting garnet growth up-pressure. Aided by mineral equilibria forward modelling, the garnet rim compositions and the Zr content of Cambrian rutile constrain peak metamorphic conditions of ∼ 17.5–19 kbar and ∼ 780–820 °C, equivalent to warm subduction thermal gradients between 410–470 °C/GPa. Garnet core compositions and the Ti content of quartz inclusions in the garnet cores constrain the pressures and temperatures for garnet nucleation to ∼ 6–7 kbar and ∼ 560–580 °C, corresponding to relatively high prograde thermal gradients between 800–965 °C/GPa. The thermal gradients determined from the south-west Tasmanian metamorphic record provide a direct window into the progressive evolution of the thermal state of the Cambrian subduction system, with the physical conditions of garnet nucleation potentially reflecting those of subduction initiation. The corresponding warm thermal gradients provide evidence for subduction initiation driven by the collapse of a pre-orogenic back-arc. This interpretation is consistent with an existing tectonic model for the Tyennan Orogeny which proposes a back-arc basin origin for the protoliths to the western Tasmanian sub-ophiolitic metamorphic sole.
The application of machine learning for pyrite discrimination establishes a robust foundation for constructing the ore-forming history of multi-stage deposits; however, published models face challenges related to limited, imbalanced datasets and oversampling. In this study, the dataset was expanded to approximately 500 samples for each type, including 508 sedimentary, 573 orogenic gold, 548 sedimentary exhalative (SEDEX) deposits, and 364 volcanogenic massive sulfides (VMS) pyrites, utilizing random forest (RF) and support vector machine (SVM) methodologies to enhance the reliability of the classifier models. The RF classifier achieved an overall accuracy of 99.8%, and the SVM classifier attained an overall accuracy of 100%. The model was evaluated by a five-fold cross-validation approach with 93.8% accuracy for the RF and 94.9% for the SVM classifier. These results demonstrate the strong feasibility of pyrite classification, supported by a relatively large, balanced dataset and high accuracy rates. The classifier was employed to reveal the genesis of the controversial Keketale Pb-Zn deposit in NW China, which has been inconclusive among SEDEX, VMS, or a SEDEX-VMS transition. Petrographic investigations indicated that the deposit comprises early fine-grained layered pyrite (Py1) and late recrystallized pyrite (Py2). The majority voting classified Py1 as the VMS type, with an accuracy of RF and SVM being 72.2% and 75%, respectively, and confirmed Py2 as an orogenic type with 74.3% and 77.1% accuracy, respectively. The new findings indicated that the Keketale deposit originated from a submarine VMS mineralization system, followed by late orogenic-type overprinting of metamorphism and deformation, which is consistent with the geological and geochemical observations. This study further emphasizes the advantages of Machine learning (ML) methods in accurately and directly discriminating the deposit types and reconstructing the formation history of multi-stage deposits.
Hurricanes are one of the most destructive natural disasters that can cause catastrophic losses to both communities and infrastructure. Assessment of hurricane risk furnishes a spatial depiction of the interplay among hazard, vulnerability, exposure, and mitigation capacity, crucial for understanding and managing the risks hurricanes pose to communities. These assessments aid in gauging the efficacy of existing hurricane mitigation strategies and gauging their resilience across diverse climate change scenarios. A systematic review was conducted, encompassing 94 articles, to scrutinize the structure, data inputs, assumptions, methodologies, perils modelled, and key predictors of hurricane risk. This review identified key research gaps essential for enhancing future risk assessments. The complex interaction between hurricane perils may be disastrous and underestimated in the majority of risk assessments which focus on a single peril, commonly storm surge and flood, resulting in inadequacies in disaster resilience planning. Most risk assessments were based on hurricane frequency rather than hurricane damage, which is more insightful for policymakers. Furthermore, considering secondary indirect impacts stemming from hurricanes, including real estate market and business interruption, could enrich economic impact assessments. Hurricane mitigation measures were the most under-utilised category of predictors leveraged in only 5% of studies. The top six predictive factors for hurricane risk were land use, slope, precipitation, elevation, population density, and soil texture/drainage. Another notable research gap identified was the potential of machine learning techniques in risk assessments, offering advantages over traditional MCDM and numerical models due to their ability to capture complex nonlinear relationships and adaptability to different study regions. Existing machine learning based risk assessments leverage random forest models (42% of studies) followed by neural network models (19% of studies), with further research required to investigate diverse machine learning algorithms such as ensemble models. A further research gap is model validation, in particular assessing transferability to a new study region. Additionally, harnessing simulated data and refining projections related to demographic and built environment dynamics can bolster the sophistication of climate change scenario assessments. By addressing these research gaps, hurricane risk assessments can furnish invaluable insights for national policymakers, facilitating the development of robust hurricane mitigation strategies and the construction of hurricane-resilient communities. To the authors’ knowledge, this represents the first literature review specifically dedicated to quantitative hurricane risk assessments, encompassing a comparison of Multi-criteria Decision Making (MCDM), numerical models, and machine learning models. Ultimately, advancements in hurricane risk assessments and modelling stand poised to mitigate potential losses to communities and infrastructure both in the immediate and long-term future.
The investigations of physical attributes of oceans, including parameters such as heat flow and bathymetry, have garnered substantial attention and are particularly valuable for examining Earth’s thermal structures and dynamic processes. Nevertheless, classical plate cooling models exhibit disparities when predicting observed heat flow and seafloor depth for extremely young and old lithospheres. Furthermore, a comprehensive analysis of global heat flow predictions and regional ocean heat flow or bathymetry data with physical models has been lacking. In this study, we employed power-law models derived from the singularity theory of fractal density to meticulously fit the latest ocean heat flow and bathymetry. Notably, power-law models offer distinct advantages over traditional plate cooling models, showcasing robust self-similarity, scale invariance, or scaling properties, and providing a better fit to observed data. The outcomes of our singularity analysis concerning heat flow and bathymetry across diverse oceanic regions exhibit a degree of consistency with the global ocean spreading rate model. In addition, we applied the similarity method to predict a higher resolution (0.1° × 0.1°) global heat flow map based on the most recent heat flow data and geological/geophysical observables refined through linear correlation analysis. Regions displaying significant disparities between predicted and observed heat flow are closely linked to hydrothermal vent fields and active structures. Finally, combining the actual bathymetry and predicted heat flow with the power-law models allows for the quantitative and comprehensive detection of anomalous regions of ocean subsidence and heat flow, which deviate from traditional plate cooling models. The anomalous regions of subsidence and heat flow show different degrees of anisotropy, providing new ideas and clues for further analysis of ocean topography or hydrothermal circulation of mid-ocean ridges.
Energy poverty in developing countries is a critical issue characterized by the lack of access to modern energy services, such as electricity and clean cooking facilities, as marked in SDG 7. This study explores the correlations between energy poverty, energy intensity, resource abundance, and income inequality, as these factors have been theorized to play important roles in influencing energy poverty in developing countries. By observing that the dataset is heterogeneous across the countries and over the time frame, we use the Method of Moments Quantile Regression (MMQR) to analyze our developing countries’ data from 2000 to 2019. Our findings indicate that energy intensity is a significant factor influencing energy poverty, suggesting that higher energy consumption relative to the sample countries can exacerbate this issue. Additionally, we observe that income inequality within the sample countries is a critical determinant of energy poverty levels, highlighting the dynamics between economic disparity and access to energy resources. Interestingly, our study reveals that resource abundance acts as a blessing rather than a curse in terms of energy poverty, implying that countries rich in natural resources may have better opportunities to combat energy deprivation. Finally, we emphasize the vital role of financial markets in addressing energy poverty on a global scale, suggesting that robust financial systems can facilitate investments and innovations aimed at improving energy access for vulnerable populations. The results from the robustness supports the empirical results obtained from the main estimation. The empirical findings of the present study advance important comprehensions for policymakers to adopt energy policies that address the complex challenges of energy poverty and promote inclusive energy access.
Cold seeps are oases for biological communities on the sea floor around hydrocarbon emission pathways. Microbial utilization of methane and other hydrocarbons yield products that fuel rich chemosynthetic communities at these sites. One such site in the cold seep ecosystem of Krishna-Godavari basin (K-G basin) along the east coast of India, discovered in Feb 2018 at a depth of 1800 m was assessed for its bacterial diversity. The seep bacterial communities were dominated by phylum Proteobacteria (57%), Firmicutes (16%) and unclassified species belonging to the family Helicobacteriaceae. The surface sediments of the seep had maximum OTUs (operational taxonomic units) (2.27 × 103) with a Shannon alpha diversity index of 8.06. In general, environmental parameters like total organic carbon (p < 0.01), sulfate (p < 0.001), sulfide (p < 0.05) and methane (p < 0.01) were responsible for shaping the bacterial community of the cold seep ecosystem in the K-G Basin. Environmental parameters play a significant role in changing the bacterial diversity richness between different cold seep environments in the oceans.
In this study, the advanced machine learning algorithm NESTORE (Next STrOng Related Earthquake) was applied to the Japan Meteorological Agency catalog (1973–2024). It calculates the probability that the aftershocks will reach or exceed a magnitude equal to the magnitude of the mainshock minus one and classifies the clusters as type A or type B, depending on whether this condition is met or not. It has been shown useful in the tests in Italy, western Slovenia, Greece, and California. Due to Japan’s high and complex seismic activity, new algorithms were developed to complement NESTORE: a hybrid cluster identification method, which uses both ETAS-based stochastic declustering and deterministic graph-based selection, and REPENESE (RElevant features, class imbalance PErcentage, NEighbour detection, SElection), an algorithm for detecting outliers in skewed class distributions, which takes in account if one class has a larger number of samples with respect to the other (class imbalance).
We document, for the first time, Mesoproterozoic-aged, continental arc magmatism in the Tasmanides. Granitoid samples intruding the Proterozoic Cape River Metamorphics in northeast Queensland contain abundant ∼ 1200 Ma igneous zircons, with early-Paleozoic metamorphic rim overgrowths. Analytical mixing between the igneous and metamorphic zircons produces cryptic discordant analyses, but the origin of said discordance is resolved with zircon Th/U ratios. Samples of the Fat Hen Creek Complex are peraluminous, calc-alkaline, S-type granitoids, that record high-grade metamorphism and trace element mobilization. The P3 and P42 intrusions are metaluminous, calc-alkaline, I-type granodiorite, which intruded the Cape River Metamorphics, and contain trace element signatures consistent with a continental-arc setting. We propose that a Mesoproterozoic continental terrane, herein referred to as the Oakvale Province, exists as basement to the Thomson Orogen. We propose several models for the formation of the Oakvale Province, with potential links to the Tarim Block, and the Yangtze Craton, during the late-Mesoproterozoic. We propose that the Oakvale Province supplied the Tasmanides with late-Mesoproterozoic detritus, and that such detritus was not solely sourced from the Musgrave Province as previously interpreted. Finally, we interpret the oroclinal bending of Paleozoic deformation and plutonic fabrics to reflect the buried extent of the Oakvale Province, and to potentially map out the Neoproterozoic rift margin associated with Rodinia break-up.
Subduction zones are critical interfaces for lithospheric volatile fluxes, where complex tectonic and geochemical interactions facilitate the release of gases and fluids from deep-seated reservoirs within the Earth’s crust. Mud volcanism, as a dynamic manifestation of these processes, contributes CH4 emissions that influence the global methane budget and impact marine ecosystems. Although ∼2000 CH4-rich mud extrusions have been documented in subduction zones globally, the geological origins and subduction-related geochemical and tectonic mechanisms driving these emissions remain poorly understood. This research examines the Makran subduction zone which hosts one of the world’s largest accretionary wedge and extensive CH4-rich mud extrusions, as a model system. Integrated geochemical, geophysical, and geological observations reveal that thermogenic CH4 and clay-rich fluidized muds originate from deeply buried Himalayan turbidites (underthrusted sediments), driven by organic-rich sediment maturation and high fluid overpressure. Key tectonic features, including thrust faults, overburden pressure of wedge-top sediments, normal faults, brittle fractures, and seismicity, facilitate CH4-rich mud extrusions into the hydrosphere and atmosphere. The extruded gases are predominantly CH4, with minor C2H6, C3H8, i-C4H10, and n-C4H10 while the mud breccia exhibits a chemical composition dominated by SiO2, Al2O3, and Fe2O3, enriched with trace elements (Rb, Zr, and V) and clay minerals, quartz, and carbonates. Geochemical indicators suggest intense chemical weathering and mature sediments classifying the mud breccia as litharenite and sub-litharenite, indicative of deep burial and compaction. These findings model the evolution of CH4-rich mud extrusions through three geological stages: (i) Eocene to Early Miocene pre-thermogenic formation of the CH4-rich source, (ii) Middle Miocene to Pliocene syn-thermogenic CH4 and fluidized mud generation, and (iii) Pleistocene to Recent post-thermogenic CH4-rich fluidized mud migration. These findings underscore the critical yet often overlooked role of subduction-related geochemical and tectonic processes in CH4 generation and emission, with significant implications for the global CH4 budget and marine ecosystems.
It has been shown that the age of minerals in which U ± Th are a major (e.g., uraninite, pitchblende and thorite) or minor (e.g., monazite, xenotime) component can be calculated from the concentrations of U ± Th and Pb rather than their isotopes, and such ages are referred to as chemical ages. Although equations for calculating the chemical ages have been well established and various computation programs have been reported, there is a lack of software that can not only calculate the chemical ages of individual analytical points but also provide an evaluation of the errors of individual ages as well as the whole dataset. In this paper, we develop a software for calculating and assessing the chemical ages of uranium minerals (CAUM), an open-source Python-based program with a friendly Graphical User Interface (GUI). Electron probe microanalysis (EPMA) data of uranium minerals are first imported from Excel files and used to calculate the chemical ages and associated errors of individual analytical points. The age data are then visualized to aid evaluating if the dataset comprises one or multiple populations and whether or not there are meaningful correlations between the chemical ages and impurities. Actions can then be taken to evaluate the errors within individual populations and the significance of the correlations. The use of the software is demonstrated with examples from published data.
At the end of the Cretaceous period, 66 million years ago, the 7 − 19 km diameter Chicxulub asteroid hit the Yucatan Peninsula in Mexico, triggering global catastrophic environmental changes and mass extinction. The contributions of this event towards changes in plate and plume geodynamics are not fully understood. Here we present a range of geological observations indicating that the impact marked a tectonic turning point in the behavior of mantle plume and plate motion in the Caribbean region and worldwide. At a regional scale, the impact coincides with the termination of seafloor spreading in the Caribbean Ridge. Shortly after the Cretaceous–Paleogene transition, magmatism associated with the Caribbean Large Igneous Province waned, and intensive Paleogene volcanism was initiated. These events happened synchronously with anomalously high mid-ocean ridge magmatism worldwide and an abrupt change in the relative motion of the South American and North American tectonic plates. The evidence for such abrupt changes in plate kinematics and plume behavior raises the possibility that the Chicxulub impact triggered a chain of effects that modified melt reservoirs, subducting plates, mantle flows, and lithospheric deformation. To explain how an asteroid impact could modify tectonic behavior, we discuss two end-member mechanisms: quasi-static and dynamic triggering mechanisms. We designed a numerical model to investigate the strain field and the relative plate motion before and after the impact. The model predicts an enhanced deformation associated with the impact, which surficially tapers off ∼ 500 km from the crater. The impact modifies the subjacent mantle flow field, contributing to long-term mantle-driven dynamic changes. Additionally, deformation associated with seismic effects may have contributed to far-field effects and global changes. We conclude that large asteroid impacts, such as the Chicxulub collision, could trigger cascading effects sufficient to disrupt and significantly modify plate geodynamics.
Understanding spatial heterogeneity in groundwater responses to multiple factors is critical for water resource management in coastal cities. Daily groundwater depth (GWD) data from 43 wells (2018–2022) were collected in three coastal cities in Jiangsu Province, China. Seasonal and Trend decomposition using Loess (STL) together with wavelet analysis and empirical mode decomposition were applied to identify tide-influenced wells while remaining wells were grouped by hierarchical clustering analysis (HCA). Machine learning models were developed to predict GWD, then their response to natural conditions and human activities was assessed by the Shapley Additive exPlanations (SHAP) method. Results showed that eXtreme Gradient Boosting (XGB) was superior to other models in terms of prediction performance and computational efficiency (R2 > 0.95). GWD in Yancheng and southern Lianyungang were greater than those in Nantong, exhibiting larger fluctuations. Groundwater within 5 km of the coastline was affected by tides, with more pronounced effects in agricultural areas compared to urban areas. Shallow groundwater (3–7 m depth) responded immediately (0–1 day) to rainfall, primarily influenced by farmland and topography (slope and distance from rivers). Rainfall recharge to groundwater peaked at 50% farmland coverage, but this effect was suppressed by high temperatures (>30 °C) which intensified as distance from rivers increased, especially in forest and grassland. Deep groundwater (>10 m) showed delayed responses to rainfall (1–4 days) and temperature (10–15 days), with GDP as the primary influence, followed by agricultural irrigation and population density. Farmland helped to maintain stable GWD in low population density regions, while excessive farmland coverage (>90%) led to overexploitation. In the early stages of GDP development, increased industrial and agricultural water demand led to GWD decline, but as GDP levels significantly improved, groundwater consumption pressure gradually eased. This methodological framework is applicable not only to coastal cities in China but also could be extended to coastal regions worldwide.
The Tongshan porphyry Cu deposit is well known as one of the most economically significant porphyry deposits in northeast China. Recently, Tongshan has become the largest porphyry Cu deposit in northeast China with the successful exploration of the concealed ore zone V. Ore zone V has the largest Cu tonnage (562 Mt @ 0.50% Cu) and extends into the eastern segment at Tongshan. Compared with ore zones I–III, which are hosted within granitic rocks in the western segment, the ore zone V mainly occurs in Duobaoshan volcanic rocks and the roof pendants of newly discovered intrusions. Here, we conducted a study of the understudied eastern ore zone and found that copper mineralization is associated with the newly discovered suites in the eastern segment at Tongshan. Two periods of ore-bearing quartz veins with different widths have been recognized, including quartz-chalcopyrite-pyrite veinlets (0.1–0.2 cm) and quartz-chalcopyrite-polymetallic sulfide wide veins (>0.5 cm). The latter veins can be divided into four stages (I–IV) of mineralization and alteration, which are closely related to the newly discovered granodiorite and dacite porphyry. Our new zircon U–Pb ages show that the granodiorite and dacite porphyry were developed between 228–223 Ma, suggesting that the overprinting porphyry copper mineralization occurred in the Triassic. The Triassic suites have adakite-like character with high Sr/Y, and show no or minimal negative Eu anomalies, indicating early dominant amphibole with limited plagioclase fractionation. For the Triassic intrusions, the high zircon Eu/Eu* (0.67–0.89), ΔFMQ (1.04 ± 0.53; where ΔFMQ is the log fO2 difference between the sample value and the fayalite-magnetite-quartz mineral buffer), hygrometer values (∼7.19 wt.% H2O) and high whole-rock Fe2O3/FeO, Sr/Y, V/Sc and 10,000×(Eu/Eu*)/Y ratios together indicate the Triassic magmas were oxidized and hydrous. These contents and ratios of the Triassic suites are significantly higher than those of the Ordovician suites (ΔFMQ = 0.74 ± 0.26, ∼5.90 wt.% H2O), suggesting that the newly discovered Triassic magmas are more oxidized and hydrous, with high potential for porphyry copper mineralization. Based on the investigation of mineralization and the above results, we proposed that multiple superimposed mineralizations can help form a large-scale deposit and the southeastern segment is a favorable exploration area at Tongshan.
This study highlights a new by-product source for cobalt by recycling Paleoproterozoic Mn deposits. We present a geochemical modeling approach utilizing Principal Component Analysis (PCA) for available geochemical data of Paleoproterozoic manganese deposits found in Africa and Brazil, which exhibit anomalous cobalt contents (up to 1200 ppm) along with other metals such as copper, nickel, and vanadium. The PCA results for the correlation coefficient matrix of the Enrichment Factor (EF) values of major and trace elements from samples of eight Mn deposits found in Africa and Brazil (Kisenge-Kamata, Moanda, Nsuta in Africa, and Azul, Buritirama, Lagoa do Riacho, Morro da Mina, and Serra do Navio in Brazil) yielded a cumulative variance of 53.3% for PC1 (34%) and PC2 (19.3%). In PC1, the highest positive loadings correspond to the variables MnEF, NiEF, and CoEF, while the highest negative loadings correspond to the variables SiEF, FeEF, KEF, TiEF, CrEF, and ZrEF. PC2 exhibits the highest positive loadings for the variables CaEF, MgEF, and PEF, while the highest negative loadings are for CuEF and VEF. The biplot diagram representation showed that clusters of vectors MnEF, NiEF, CoEF, VEF, and CuEF influence samples of Mn-carbonate rock, Mn-carbonate–silicate rock, Mn-silicate rock, and Mn-carbonate-siliciclastic rock, all with high CoEF values (up to 414). The cluster of vectors CaEF, MgEF, and PEF significantly influence carbonate rock and dolomite marble, which have low CoEF values (close to 0). The cluster of vectors SiEF, FeEF, KEF, TiEF, CrEF, and ZrEF strongly influences siliciclastic rock, which exhibits low CoEF values. On the other hand, the cluster of vectors CuEF and VEF influences oxidized Mn ore, which exhibits CoEF values of up to 108. The results reveal a dichotomy regarding the origin of cobalt and other metal enrichments in these deposits linked to the Mn redox cycle. This process involves the formation of Mn-oxyhydroxides with the adsorption of Co and other metals under oxic conditions, followed by the burial of these Mn oxides in an anoxic diagenetic environment, where microbial sulfate reduction leads to the nucleation of Mn-carbonates and the formation of metal-rich sulfides (Fe, Co, Ni, V). Additionally, detrital input and sulfide phases (e.g., framboidal pyrite) for the formation of Mn-rich siliciclastic rocks associated with Mn-carbonate rocks are evidenced by proxies SiEF, FeEF, KEF, TiEF, CrEF, and ZrEF. This new exploration approach, supported by geochemical modeling through PCA, enhances our understanding of the genesis of these Paleoproterozoic manganese deposits and highlights a new route for cobalt exploration. In the increasing global demand for cobalt, particularly in applications involving electric vehicle batteries and energy storage, exploring these deposits emerges as an alternative source to produce these critical metals.
This study focuses on the assessment of ecosystem health (EH), ecosystem services (ES), and ecosystem risk (ER) in East Kolkata Wetland (EKW). A comprehensive framework on the EH, ES and ER has been developed using remote sesning and geo-spatial techniques for the year 2000, 2005, 2010, 2015, and 2020. The study also assessed ecosystem structure and fragmentation using landscape metrics calculated using fragstats, which showed a significant influence of land use and land cover (LULC) changes on the wetland’s ecological integrity. The study revealed that 6.86% of EKW fallen under a very low EH zone, while 20% was categorized as having very high EH. Spatio-temporal analysis of ES indicated that 30% of the area had very low ES value, with only 8% exhibiting very high ES. ER assessment revealed that 7% of the study area was highly ER, while 12% identified within a high ER zone, reflecting frequent LULC changes. The correlation analysis highlighted strong negative relationships between landscape deviation degree (LDD) and EH (−0.971), and between normalized difference water index (NDWI) and normalized difference vegetation index (NDVI) (−0.991). Additionally, landscape metrics such as the number of patches (NP) and the largest patch index (LPI) exhibited significant correlations, emphasizing the impact of fragmentation on EH and resilience. This comprehensive assessment underscores the importance of integrated approaches to monitor and manage wetland ecosystems, particularly in urban areas facing significant environmental stressors. The findings are crucial for informed decision-making and sustainable management of the wetland ecosystems, particularly in the cities of the global south.